1.7 KiB
1.7 KiB
Optimize memory usage
Funkwhale has a reasonable memory footprint. If you're running Funkwhale on a limited device, you can use these tweaks to reduce the footprint.
Reduce workers concurrency
Funkwhale uses Celery to handle asynchronous tasks. By default, Celery spawns a worker per CPU core. This can lead to higher memory usage.
You can set the number of workers using the CELERYD_CONCURRENCY variable in your .env file. For example, a value of CELERYD_CONCURRENCY=1 spawns a single worker.
Reducing the number of celery workers slows down the handling of asynchronous tasks. On larger instances, this can cause performance problems.
Switch to solo pool execution
Celery uses a prefork pool by default. This enables the server to process many tasks at the same time. You can switch to a solo pool which handles tasks one at a time. This reduces memory overhead but removes the ability to process tasks concurrently.
1. Open your `funkwhale-worker` unit file in an editor.
```{code} bash
sudo nano /etc/systemd/system/funkwhale-worker.service
```
2. Add the `--pool=solo` flag to the `ExecStart` line of your unit file.
```{code} text
ExecStart=/srv/funkwhale/.local/bin/poetry run celery -A --pool=solo funkwhale_api.taskapp worker -l INFO --concurrency=${CELERYD_CONCURRENCY}
```
3. Restart the Celery service.
```{code} bash
sudo systemctl restart funkwhale-worker.service
```
1. Add the `--pool=solo` flag to the `celerybeat` command in `docker-compose.yml`.
```{code} yaml
celerybeat:
…
command: celery -A --pool=solo funkwhale_api.taskapp beat --pidfile= -l INFO
```
2. Restart Celery.
```{code} bash
docker-compose restart celerybeat
```